Embedding margins are useful if we want to use a \(k\) -nearest neighbour classifier as a post-hoc training procedure.
Such post-hoc training procedures have proven successful in long-tail settings [1]}.
More fundamentally, however,
one may justify the regularisation of these embeddings from the perspective of approximating the Bayes-optimal decision boundary, as we now see.
